As well known in the art, stereo matching refers to a technology of extracting 3D image information from two images produced by two cameras installed on the right and left sides. The stereo matching enables direct calculation of a distance between the location of a camera and the actual location of a specific pattern by extracting a disparity, i.e. a difference between the location of the specific pattern in one image and the location of the specific pattern in another image, in the same principle as that of two eyes of a person for acquisition of information about a distance from a specific object or an image pattern.
However, unlike the principle of two eyes of a person that enables acquisition of information about a distance of the center portion of an entire image, stereo matching technique requires a large number of calculations because all information about distances from the entire image are calculated, and causes noise in an output disparity because the characteristics of images acquired by two cameras generally are not completely identical with each other and there may exist a dark area or an area where a specific pattern cannot be clearly discriminated in the images.
Meanwhile, a system suggested for processing of stereo matching includes two cameras each having an image sensor by which a digital image is captured, a pre-processing unit primarily processing two captured images, and a stereo matching unit.
In such a system, all processing other than the processing of two cameras may be carried out through embedded software in a personal computer in accordance with its purpose, or one or all of a preprocessing unit and a stereo matching unit may be manufactured using dedicated hardware. The former may easily use various algorithms but has a slow processing speed, and the latter may use only a specific limited algorithm but has a fast processing speed.
In particular, a stereo vision system using a stereo matching technology may be used in wide fields because it can acquire information about distances from all objects in an image, as compared with the other stereo matching technologies using only one image. For this reason, it has already been used or is expected to be used in a future in a vision unit of a robot, a collision prevention system of a vehicle, a game, and a military equipment, enabling acquisition of useful information.
Meanwhile, such a stereo vision system using a stereo is matching technology may be realized using software in a personal computer or a workstation when applied to a mobile system or may be realized using dedicated hardware logic on a field programmable gate array (FPGA). Currently, there are commercialized stereo vision systems such as ‘Bumblebee’ which is available from Point Grey Research Inc. in USA and ‘Design STOC’ that process stereo vision in a personal computer, which is available from Videre Design LLC in USA and ‘DeepSea G2 Vision System’ that processes stereo vision in a dedicated chip, which is available from Tyzx Inc. in USA.
Referring to FIG. 1, there is shown a process of processing stereo vision by a conventional stereo vision system. When right and left images are produced by right and left cameras in step 102, pre-processing of the right and left images is carried out to make brightnesses of the right and left images identical and epipolar lines thereof coincide with each other in step 104, stereo matching is carried out to create a disparity between pixels of the right and left images representing a same object in step 106, post-processing of distance information of the right and left images, such as a projection, a segmentation, and a filtering, is carried out in step 108 after removing noise from the disparity, and stereo image information is then acquired in step 110. In the above-mentioned process, information about the distance between an object and a background, and the shape and direction of the object may be utilized through the stereo image information.
In other words, the conventional stereo vision system acquires image information by carrying out pre-processing, stereo matching, and post-processing of a stereo image (i.e. right and left images). However, since such a conventional stereo vision system runs an algorithm in a personal computer or on an FPGA and is mounted to a mobile system, e.g., a robot, it requires high cost and high power consumption. Furthermore, when the conventional stereo vision system is embedded in the mobile system, it is necessary to always supply power to a stereo matching scheme of the stereo vision system in order for recognition of situation and navigation.